AI Inference & GPU Infrastructure Engineering

The Production AI Inference Challenge

Running generative AI and LLM inference is fundamentally different from traditional web microservices. Model weights range from 7GB to over 140GB, GPU capacity is scarce and expensive ($2.50 to $30+ per hour per node), and standard auto-scaling mechanisms take 5 to 10 minutes to pull weights and initialize CUDA contexts, creating disastrous latency spikes and timeouts.

Many engineering teams leave expensive NVIDIA H100, A100, L40S, or G5 GPU instances running 24/7 at an average utilization of just 15% to 25%, resulting in astronomical monthly AWS bills with poor unit economics.

What We Architect & Optimize

We engineer high-efficiency inference infrastructure tailored to your models, traffic patterns, and latency requirements:

• High-Performance Inference Engines: Production deployment and tuning of vLLM, NVIDIA Triton Inference Server, TensorRT-LLM, and TGI. We optimize continuous batching, PagedAttention, KV cache management, and speculative decoding to maximize tokens-per-second per dollar.
• Intelligent GPU Autoscaling with Karpenter: Custom Karpenter NodePools configured with multi-family GPU fallback (G5, G6, L4, A10G). Scale GPU worker nodes from zero to capacity in under 90 seconds, and consolidate underutilized nodes without dropping active streaming connections.
• GPU Slicing & Virtualization: Where full GPU dedication is wasteful, we implement NVIDIA Multi-Instance GPU (MIG) and GPU time-slicing to safely co-locate embedding models, rerankers, and smaller LLMs on shared physical hardware with complete hardware isolation.
• Fast Model Weight Distribution: Slash cold starts from 8 minutes down to under 25 seconds using shared high-throughput NVMe caches, AWS Mountpoint for Amazon S3, and pre-warmed image daemonsets.
• AWS Inferentia & Trainium Migration: For high-volume transformer inference, we benchmark and migrate compatible models to AWS Inferentia2 (inf2) instances, slashing inference compute costs by up to 70% compared to equivalent NVIDIA GPUs.

AI FinOps & Inference Unit Economics

You cannot scale an AI-powered SaaS company without visibility into inference unit economics. We integrate AI FinOps frameworks directly into your observability stack:

• Cost per Million Tokens: Real-time dashboards calculating prompt vs. completion token costs per model and per customer tenant.
• GPU Utilization Metrics: Prometheus and DCGM exporters tracking SM (Streaming Multiprocessor) activity, memory bandwidth saturation, and tensor core utilization.
• Budget Guardrails & Rate Limiting: Automated circuit breakers in Kubernetes ingress and Envoy gateways to prevent runaway loops or scrapers from draining GPU budgets overnight.
• Spot GPU Strategy: Fault-tolerant inference replica architecture running on diversified Spot GPU pools with automated on-demand failover for 60%+ compute discounts.

Enterprise Security & Compliance

Inference infrastructure handles sensitive enterprise and customer prompt data. Our platform builds adhere to stringent security baselines:

• Isolated private VPC subnets with zero public IP exposure for GPU compute nodes.
• Model weights stored in customer-owned S3 buckets with KMS customer-managed encryption keys.
• EKS Pod Identity and least-privilege IAM policies ensuring inference pods have zero cluster-wide credentials.
• Audit logging of model access conforming to SOC 2 Type II, HIPAA, and ISO 27001 requirements.

Get Started

Whether you are launching your first self-hosted model cluster or spending tens of thousands per month on unoptimized cloud GPUs, our senior platform engineers can help you design a fast, resilient, cost-effective inference engine.